Table of Contents

Multicodes

Set up

library(gtsummary)
library(tidyverse)
library(srvyr)
library(flextable)
library(officer)
library(labelled)
library(rlang)

Typical theme for gtsummary()

# For all I use the compact theme.
theme_gtsummary_compact()

## Setting theme "Compact"

Load Round 10 UK European Social Survey dataset.

ddir <- paste0(getwd())
load(paste0(ddir,"/ESS_UK_snippet_Round 10.RData"))
glimpse(ess10)

## Rows: 1,149
## Columns: 39
## $ idno         <dbl> 50002, 50008, 50021, 50023, 50033, 50052, 50100, 50129, 50191, 50242, 50297, 50344, 50347, 50378, …
## $ anweight     <dbl> 2.215882, 4.085495, 3.973622, 8.731745, 3.137663, 2.000416, 4.206257, 1.966309, 1.802065, 6.647645…
## $ stratum      <dbl> 1463, 1428, 1501, 1478, 1450, 1456, 1412, 1393, 1491, 1465, 1372, 1473, 1398, 1430, 1410, 1380, 14…
## $ psu          <dbl> 10279, 10236, 10327, 10300, 10387, 10102, 10263, 10413, 10150, 10115, 10208, 10287, 10118, 10401, …
## $ atchctr      <fct> 7, Very emotionally attached, 7, 4, 7, 6, 2, 8, 8, Not at all emotionally attached, 9, Very emotio…
## $ stflife      <fct> 8, 8, 7, 8, 5, 6, 6, 8, 8, 8, Extremely satisfied, 9, 7, 7, 6, 5, 8, 7, 7, 4, 5, Extremely satisfi…
## $ stfgov       <fct> 2, 7, 4, 4, 3, Extremely dissatisfied, 3, 5, 1, Extremely dissatisfied, 5, 5, 3, 2, 1, 4, 8, 5, 1,…
## $ psppsgva     <fct> Very little, Some, Very little, Some, Very little, Very little, Very little, Very little, Very lit…
## $ actrolga     <fct> Quite able, Not at all able, Not at all able, A little able, Not at all able, A little able, Quite…
## $ cptppola     <fct> Not at all confident, Not at all confident, Not at all confident, A little confident, A little con…
## $ lrscale      <fct> 5, 9, 5, 2, 6, 5, 6, 4, 4, 4, 5, 4, 2, 2, 3, 5, 6, 5, 2, 5, 5, 7, 3, NA, 3, NA, NA, 6, 4, 8, 5, 5,…
## $ gincdif      <fct> Disagree, Disagree, Neither agree nor disagree, Agree, Agree, Agree, Agree strongly, Agree, Agree …
## $ ppltrst      <fct> 7, 8, 4, 4, 6, 2, 6, 9, 7, 6, 4, 4, 7, 4, 7, 5, 6, 5, 8, 5, 4, 5, 8, 5, 6, 5, 5, 7, 8, 3, 3, 8, 8,…
## $ trstplt      <fct> 2, 7, 3, 2, No trust at all, 1, No trust at all, 6, 4, 7, 3, 1, 5, 2, 5, 3, 7, 5, 2, 5, No trust a…
## $ imwbcnt      <fct> 7, 3, 6, Better place to live, 4, 5, 9, 5, 7, 9, 7, 6, 8, 8, 8, 5, 5, 9, 9, 5, 6, 3, 8, 8, 6, 8, W…
## $ sclmeet      <fct> Once a month, Several times a month, Less than once a month, Several times a week, Once a month, N…
## $ gndr         <fct> Male, Female, Male, Female, Female, Female, Female, Male, Female, Male, Male, Female, Female, Male…
## $ agea         <dbl> 72, 83, 69, 21, 64, 70, 51, 86, 41, 57, 62, 67, 72, 19, 46, 37, 55, 73, 27, 28, 52, 15, 70, 44, 76…
## $ hincfel      <fct> Living comfortably on present income, Living comfortably on present income, Coping on present inco…
## $ ctzcntr      <fct> Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes,…
## $ brncntr      <fct> Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes,…
## $ feethngr     <fct> Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, No, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, …
## $ rlgblg       <fct> No, No, Yes, No, No, Yes, No, No, No, No, No, No, No, Yes, No, No, Yes, No, No, No, No, No, Yes, Y…
## $ rlgblge      <fct> No, Yes, NA, Yes, No, NA, No, No, No, Yes, No, Yes, No, NA, No, No, NA, No, Yes, No, No, Yes, NA, …
## $ vote         <fct> Yes, Yes, Yes, No, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Not eligible to vote, Yes, Yes, Yes…
## $ prtvtdgb     <fct> Labour, Conservative, Conservative, NA, NA, NA, Liberal Democrat, Labour, Labour, Scottish Nationa…
## $ mainact      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Retired", NA, "Paid work", NA, "Education", NA, "Paid work", …
## $ mnactic      <fct> "Retired", "Retired", "Retired", "Education", "Paid work", "Retired", "Paid work", "Retired", "Pai…
## $ atchctr_num  <dbl> 7, 10, 7, 4, 7, 6, 2, 8, 8, 0, 9, 10, 10, 4, 4, 8, 10, 10, 5, 10, 7, 5, 10, 8, 2, 5, 10, 3, 7, 8, …
## $ stflife_num  <dbl> 8, 8, 7, 8, 5, 6, 6, 8, 8, 8, 10, 9, 7, 7, 6, 5, 8, 7, 7, 4, 5, 10, 9, 7, 3, 3, 10, 2, 4, 8, 6, 5,…
## $ stfgov_num   <dbl> 2, 7, 4, 4, 3, 0, 3, 5, 1, 0, 5, 5, 3, 2, 1, 4, 8, 5, 1, 5, 0, 7, 3, 7, 0, 2, 3, 1, 8, 7, 3, 2, 4,…
## $ psppsgva_num <dbl> 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 4, 4, 3, 2, 3, 2, 3, 2, 2, 3, 3, 1, 2, 1, 2, 2, 2, 4,…
## $ actrolga_num <dbl> 3, 1, 1, 2, 1, 2, 3, 4, 3, 3, 2, 1, 2, 1, 2, 4, 4, 3, 2, 1, 1, 3, 3, 1, 2, 2, 2, 1, 1, 2, 2, 3, 4,…
## $ cptppola_num <dbl> 1, 1, 1, 2, 2, 1, 4, 4, 4, 4, 3, 1, 3, 1, 3, 3, 4, 2, 3, 1, 1, 3, 3, 2, 2, 5, 2, 1, 1, 4, 1, 4, 4,…
## $ lrscale_num  <dbl> 5, 9, 5, 2, 6, 5, 6, 4, 4, 4, 5, 4, 2, 2, 3, 5, 6, 5, 2, 5, 5, 7, 3, NA, 3, NA, NA, 6, 4, 8, 5, 5,…
## $ gincdif_num  <dbl> 4, 4, 3, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 4, 3, 3, 1, 5, 2, 1, 3, 3, 1, 3, 2,…
## $ ppltrst_num  <dbl> 7, 8, 4, 4, 6, 2, 6, 9, 7, 6, 4, 4, 7, 4, 7, 5, 6, 5, 8, 5, 4, 5, 8, 5, 6, 5, 5, 7, 8, 3, 3, 8, 8,…
## $ trstplt_num  <dbl> 2, 7, 3, 2, 0, 1, 0, 6, 4, 7, 3, 1, 5, 2, 5, 3, 7, 5, 2, 5, 0, 5, 6, 4, 1, 0, 0, 0, 3, 2, 2, 3, 5,…
## $ imwbcnt_num  <dbl> 7, 3, 6, 10, 4, 5, 9, 5, 7, 9, 7, 6, 8, 8, 8, 5, 5, 9, 9, 5, 6, 3, 8, 8, 6, 8, 0, 3, 2, 7, 5, 7, 9…

svy_df <- ess10 %>% as_survey_design(weights = anweight)

# List all .R files in ddir 
file_paths <- list.files(path = ddir, pattern = "\\.R$", full.names = TRUE, all.files = T) %>% 
  map_chr(., ~str_remove(.x, ddir)) %>% 
  grep("_interim", ., value =T, invert = T) # Drop interim functions, not used.

# Set of functions to be loaded. 
file_paths

##  [1] "/.col_width.R"             "/addh.R"                   "/base_information.R"       "/bases_equal.R"           
##  [5] "/create_bases.R"           "/gtsummary_table_notes.R"  "/mtotals.R"                "/multicodes.R"            
##  [9] "/nest_multicodes.R"        "/nested_tables.R"          "/prepare_base_for_table.R" "/revise_labels.R"         
## [13] "/splitter_present.R"       "/style_hux_df.R"           "/tables.R"                 "/totals.R"

Load functions

# Load all functions (make sure no other .R files in this location)
map(file_paths, ~source(paste0(ddir,.x)))

Load additional binary data

The code below loads some additional data, for use in multicoded tables.

load("/Users/joecrowley/R/Data/ESS_UK/ESS_UK_R_data_file.RData")

# Use set of variables on location people can access internet from
ess %>% select(starts_with("acc")) %>% map_df(.,var_label) %>% pivot_longer(cols = everything())

name	value
accdng	Feel accomplishment from what I do
accalaw	Acceptable for country to have a strong leader above the law
acchome	Location able to access the internet: Home
accwrk	Location able to access the internet: Workplace
accmove	Location able to access the internet: On the move
accoth	Location able to access the internet: Some other place
accnone	Location able to access the internet: None of the above
accref	Location able to access the internet: Refusal
accdk	Location able to access the internet: Don't know
accna	Location able to access the internet: No answer

# Identify variable names
int_loc <- ess %>% select(starts_with("acc")) %>% names
int_loc <- int_loc[3:10]
int_loc

## [1] "acchome" "accwrk"  "accmove" "accoth"  "accnone" "accref"  "accdk"   "accna"

ess %>% select(all_of(int_loc)) %>% map_df(.,var_label) %>% pivot_longer(cols = everything())

name	value
acchome	Location able to access the internet: Home
accwrk	Location able to access the internet: Workplace
accmove	Location able to access the internet: On the move
accoth	Location able to access the internet: Some other place
accnone	Location able to access the internet: None of the above
accref	Location able to access the internet: Refusal
accdk	Location able to access the internet: Don't know
accna	Location able to access the internet: No answer

ess10 %>% select(ctzcntr, brncntr,feethngr) %>% look_for() %>% convert_list_columns_to_character() %>% flextable

pos	variable	label	col_type	missing	levels	value_labels
1	ctzcntr	Citizen of country	fct	1	Yes; No; Refusal; Don't know; No answer
2	brncntr	Born in country	fct	1	Yes; No; Refusal; Don't know; No answer
3	feethngr	Feel part of same race or ethnic group as most people in country	fct	9	Yes; No; Refusal; Don't know; No answer

Overview of process

Some checks

• all have same base size?
• contains splitter in variable labels? (: )
  
• not contains multiple splitters.

Steps.

1. Checks above,
   
2. Relabel variables, store stem
3. Create unweighted base variable,
   
4. Create vector of variables to use,
5. Create table,
6. Optional, add caption,
7. Optional, add CI.

ess %>% select(all_of(int_loc)) %>% look_for(details = "none")

pos	variable	label
1	acchome	Location able to access the internet: Home
2	accwrk	Location able to access the internet: Workplace
3	accmove	Location able to access the internet: On the move
4	accoth	Location able to access the internet: Some other place
5	accnone	Location able to access the internet: None of the above
6	accref	Location able to access the internet: Refusal
7	accdk	Location able to access the internet: Don't know
8	accna	Location able to access the internet: No answer

Data set up

These functions are just going to expect well managed data.

You can provide variables which are either:

• yes / no.
  
• 0 / 1.
  
• TRUE / FALSE

All variables must have those levels, even if not observed for a particular variable.

# Variables must be factors, drop unused levels..
ess3 <- ess %>% 
  mutate(across(all_of(int_loc), ~fct_drop(to_factor(.x))))

# gtsummary expects yes/no, 0/1 or TRUE/FALSE for dichotomous variables
ess3$acchome %>% levels # we have 'Not marked' / 'Marked'

## [1] "Not marked" "Marked"

ess3 <- ess3 %>% 
  mutate(across(all_of(int_loc), ~fct_recode(.x, 
        "Yes" = "Marked", "No" = "Not marked")))

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(all_of(int_loc), ~fct_recode(.x, Yes = "Marked", No = "Not marked"))`.
## Caused by warning:
## ! Unknown levels in `f`: Marked
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

ess3$acchome %>% levels

## [1] "No"  "Yes"

# Note they do all need to have the two levels...
# When adding factor levels remember to use forcats to preserve variable labels
# If done with factor() variable labels will be dropped. 
ess3 %>% select(all_of(int_loc)) %>% map(., levels)

## $acchome
## [1] "No"  "Yes"
## 
## $accwrk
## [1] "No"  "Yes"
## 
## $accmove
## [1] "No"  "Yes"
## 
## $accoth
## [1] "No"  "Yes"
## 
## $accnone
## [1] "No"  "Yes"
## 
## $accref
## [1] "No"
## 
## $accdk
## [1] "No"
## 
## $accna
## [1] "No"

ess3 <- ess3 %>% mutate(across(all_of(int_loc), ~fct_expand(.x, c("Yes", "No")))) 
ess3 %>%  select(all_of(int_loc)) %>% map(., levels)

## $acchome
## [1] "No"  "Yes"
## 
## $accwrk
## [1] "No"  "Yes"
## 
## $accmove
## [1] "No"  "Yes"
## 
## $accoth
## [1] "No"  "Yes"
## 
## $accnone
## [1] "No"  "Yes"
## 
## $accref
## [1] "No"  "Yes"
## 
## $accdk
## [1] "No"  "Yes"
## 
## $accna
## [1] "No"  "Yes"

var_label(ess %>% select(all_of(int_loc)))

## $acchome
## [1] "Location able to access the internet: Home"
## 
## $accwrk
## [1] "Location able to access the internet: Workplace"
## 
## $accmove
## [1] "Location able to access the internet: On the move"
## 
## $accoth
## [1] "Location able to access the internet: Some other place"
## 
## $accnone
## [1] "Location able to access the internet: None of the above"
## 
## $accref
## [1] "Location able to access the internet: Refusal"
## 
## $accdk
## [1] "Location able to access the internet: Don't know"
## 
## $accna
## [1] "Location able to access the internet: No answer"

# Age
ess3 %>% count(age)

age	n
15	1
15.0799999237061	1
15.1700000762939	3
15.25	1
15.3299999237061	1
15.4200000762939	1
15.5	1
15.5799999237061	3
15.6700000762939	1
15.75	4
15.8299999237061	1
15.9200000762939	4
16	1
16.0799999237061	1
16.1700000762939	1
16.25	3
16.3299999237061	4
16.4200000762939	1
16.5	3
16.5799999237061	2
16.6700000762939	3
16.75	1
16.8299999237061	4
16.9200000762939	2
17.1700000762939	1
17.25	3
17.4200000762939	3
17.5	8
17.5799999237061	2
17.6700000762939	4
17.75	4
17.8299999237061	2
18.0799999237061	6
18.1700000762939	1
18.25	2
18.3299999237061	3
18.4200000762939	1
18.5	2
18.5799999237061	4
18.6700000762939	1
18.75	2
18.8299999237061	1
18.9200000762939	1
19	1
19.0799999237061	1
19.1700000762939	2
19.25	2
19.3299999237061	2
19.4200000762939	3
19.5	3
19.5799999237061	2
19.6700000762939	1
19.75	1
19.8299999237061	3
19.9200000762939	3
20	7
20.0799999237061	2
20.1700000762939	1
20.25	1
20.3299999237061	2
20.4200000762939	3
20.5	1
20.5799999237061	3
20.6700000762939	3
20.75	2
20.8299999237061	4
20.9200000762939	4
21	3
21.0799999237061	2
21.1700000762939	4
21.25	2
21.4200000762939	4
21.5	1
21.5799999237061	5
21.8299999237061	5
21.9200000762939	1
22	1
22.0799999237061	1
22.1700000762939	4
22.25	2
22.3299999237061	1
22.4200000762939	2
22.5	2
22.5799999237061	2
22.6700000762939	2
22.75	1
22.8299999237061	1
23.0799999237061	2
23.1700000762939	3
23.25	2
23.3299999237061	1
23.4200000762939	2
23.5	2
23.5799999237061	4
23.6700000762939	4
23.8299999237061	4
23.9200000762939	5
24	1
24.0799999237061	2
24.1700000762939	6
24.25	2
24.3299999237061	4
24.4200000762939	1
24.5	1
24.5799999237061	2
24.6700000762939	8
24.8299999237061	1
24.9200000762939	5
25.0799999237061	4
25.1700000762939	7
25.25	1
25.3299999237061	3
25.4200000762939	2
25.5	1
25.5799999237061	3
25.6700000762939	2
25.75	3
25.9200000762939	5
26	5
26.0799999237061	1
26.1700000762939	4
26.25	3
26.3299999237061	1
26.4200000762939	3
26.5	6
26.5799999237061	2
26.6700000762939	2
26.75	3
26.8299999237061	1
26.9200000762939	1
27	4
27.0799999237061	2
27.1700000762939	1
27.25	8
27.3299999237061	2
27.4200000762939	3
27.5	2
27.5799999237061	2
27.6700000762939	1
27.75	2
27.8299999237061	1
28	1
28.0799999237061	4
28.1700000762939	5
28.3299999237061	4
28.4200000762939	1
28.5	3
28.5799999237061	1
28.75	1
28.8299999237061	3
28.9200000762939	2
29	6
29.1700000762939	3
29.25	2
29.3299999237061	2
29.4200000762939	4
29.5	1
29.5799999237061	5
29.6700000762939	4
29.75	5
29.8299999237061	3
29.9200000762939	3
30	3
30.0799999237061	2
30.1700000762939	3
30.25	3
30.3299999237061	2
30.4200000762939	3
30.5	3
30.5799999237061	4
30.6700000762939	3
30.75	8
30.8299999237061	5
30.9200000762939	2
31	4
31.1700000762939	1
31.25	3
31.3299999237061	2
31.4200000762939	5
31.5	1
31.5799999237061	2
31.6700000762939	3
31.75	2
31.8299999237061	2
31.9200000762939	3
32	4
32.0800018310547	2
32.1699981689453	3
32.25	2
32.3300018310547	1
32.4199981689453	4
32.5	5
32.5800018310547	3
32.6699981689453	3
32.75	2
32.8300018310547	3
32.9199981689453	8
33	7
33.0800018310547	1
33.1699981689453	5
33.25	3
33.3300018310547	2
33.4199981689453	4
33.5	5
33.5800018310547	4
33.6699981689453	7
33.75	1
33.8300018310547	4
33.9199981689453	1
34	4
34.0800018310547	5
34.1699981689453	2
34.25	3
34.3300018310547	2
34.4199981689453	3
34.5	1
34.5800018310547	4
34.6699981689453	1
34.75	3
34.8300018310547	6
34.9199981689453	5
35	7
35.0800018310547	3
35.1699981689453	4
35.25	4
35.3300018310547	7
35.4199981689453	1
35.5	3
35.5800018310547	2
35.6699981689453	2
35.75	2
35.8300018310547	3
35.9199981689453	2
36	7
36.0800018310547	2
36.1699981689453	6
36.25	5
36.3300018310547	3
36.4199981689453	3
36.5	4
36.5800018310547	5
36.6699981689453	2
36.75	3
36.8300018310547	1
36.9199981689453	3
37	1
37.0800018310547	8
37.1699981689453	6
37.25	5
37.3300018310547	5
37.5	5
37.5800018310547	3
37.6699981689453	5
37.75	3
37.8300018310547	6
37.9199981689453	3
38	5
38.0800018310547	6
38.1699981689453	2
38.25	4
38.3300018310547	2
38.4199981689453	2
38.5	1
38.5800018310547	7
38.6699981689453	1
38.75	2
38.8300018310547	4
38.9199981689453	3
39	3
39.0800018310547	6
39.1699981689453	5
39.25	3
39.3300018310547	3
39.4199981689453	7
39.5	2
39.5800018310547	6
39.6699981689453	4
39.75	2
39.8300018310547	2
39.9199981689453	3
40	2
40.0800018310547	4
40.1699981689453	2
40.25	4
40.3300018310547	3
40.4199981689453	4
40.5	3
40.5800018310547	2
40.6699981689453	8
40.75	7
40.8300018310547	1
40.9199981689453	8
41	6
41.0800018310547	5
41.1699981689453	1
41.25	3
41.3300018310547	1
41.4199981689453	8
41.5	6
41.5800018310547	6
41.6699981689453	3
41.8300018310547	3
42	2
42.0800018310547	5
42.1699981689453	4
42.25	4
42.3300018310547	11
42.4199981689453	3
42.5	4
42.5800018310547	6
42.6699981689453	1
42.75	3
42.8300018310547	3
42.9199981689453	3
43	4
43.0800018310547	3
43.1699981689453	4
43.25	3
43.3300018310547	4
43.4199981689453	1
43.5	2
43.5800018310547	6
43.6699981689453	2
43.75	2
43.8300018310547	3
44	4
44.1699981689453	5
44.25	3
44.3300018310547	4
44.4199981689453	6
44.5	6
44.5800018310547	5
44.6699981689453	8
44.75	3
44.8300018310547	5
44.9199981689453	4
45	2
45.0800018310547	5
45.1699981689453	1
45.25	2
45.3300018310547	4
45.4199981689453	3
45.5	8
45.5800018310547	3
45.6699981689453	3
45.75	6
45.8300018310547	5
46	2
46.0800018310547	2
46.1699981689453	3
46.25	4
46.3300018310547	3
46.4199981689453	4
46.5	2
46.5800018310547	6
46.6699981689453	3
46.75	1
46.8300018310547	2
46.9199981689453	2
47	1
47.0800018310547	1
47.1699981689453	3
47.25	2
47.3300018310547	3
47.4199981689453	2
47.5	1
47.5800018310547	2
47.6699981689453	3
47.75	4
47.8300018310547	7
47.9199981689453	5
48	2
48.1699981689453	2
48.25	5
48.3300018310547	3
48.4199981689453	3
48.5	5
48.5800018310547	6
48.6699981689453	4
48.75	3
48.8300018310547	3
48.9199981689453	5
49	5
49.0800018310547	1
49.1699981689453	4
49.25	3
49.3300018310547	2
49.4199981689453	2
49.5	4
49.5800018310547	4
49.6699981689453	6
49.75	4
49.8300018310547	2
49.9199981689453	2
50	4
50.0800018310547	2
50.1699981689453	4
50.25	1
50.3300018310547	4
50.4199981689453	5
50.5	2
50.5800018310547	2
50.6699981689453	4
50.75	3
50.8300018310547	1
50.9199981689453	1
51	3
51.0800018310547	1
51.1699981689453	5
51.25	1
51.3300018310547	3
51.5	5
51.5800018310547	3
51.6699981689453	2
51.75	3
51.8300018310547	1
51.9199981689453	8
52	1
52.0800018310547	5
52.1699981689453	2
52.25	5
52.3300018310547	1
52.4199981689453	5
52.5	2
52.5800018310547	4
52.6699981689453	5
52.75	3
52.8300018310547	2
52.9199981689453	1
53	3
53.0800018310547	4
53.1699981689453	4
53.25	3
53.3300018310547	4
53.4199981689453	2
53.5800018310547	2
53.6699981689453	1
53.9199981689453	1
54	2
54.0800018310547	5
54.1699981689453	4
54.25	2
54.3300018310547	1
54.4199981689453	7
54.5	4
54.5800018310547	4
54.6699981689453	1
54.75	6
54.8300018310547	2
54.9199981689453	3
55	2
55.0800018310547	5
55.1699981689453	2
55.25	2
55.4199981689453	2
55.5	3
55.5800018310547	2
55.6699981689453	2
55.75	1
55.8300018310547	4
55.9199981689453	4
56	5
56.0800018310547	1
56.1699981689453	1
56.25	4
56.3300018310547	2
56.4199981689453	5
56.5	2
56.5800018310547	3
56.75	2
56.8300018310547	4
56.9199981689453	6
57	3
57.0800018310547	2
57.1699981689453	5
57.25	4
57.3300018310547	3
57.4199981689453	7
57.5	2
57.5800018310547	5
57.6699981689453	2
57.75	8
57.8300018310547	2
57.9199981689453	4
58.0800018310547	3
58.1699981689453	4
58.25	5
58.3300018310547	2
58.4199981689453	2
58.5	3
58.5800018310547	2
58.6699981689453	4
58.75	1
58.8300018310547	8
58.9199981689453	2
59	2
59.0800018310547	2
59.1699981689453	3
59.25	8
59.3300018310547	4
59.4199981689453	7
59.5	7
59.5800018310547	5
59.6699981689453	3
59.75	8
59.8300018310547	4
59.9199981689453	2
60	5
60.0800018310547	1
60.1699981689453	8
60.25	2
60.3300018310547	3
60.4199981689453	5
60.5	1
60.5800018310547	5
60.6699981689453	1
60.75	2
60.8300018310547	2
60.9199981689453	4
61	2
61.1699981689453	3
61.25	7
61.3300018310547	4
61.4199981689453	5
61.5	5
61.5800018310547	5
61.6699981689453	7
61.75	4
61.8300018310547	1
61.9199981689453	5
62	1
62.0800018310547	1
62.1699981689453	2
62.25	5
62.3300018310547	3
62.4199981689453	4
62.5	4
62.5800018310547	5
62.6699981689453	2
62.75	1
62.8300018310547	3
62.9199981689453	1
63	2
63.0800018310547	1
63.1699981689453	4
63.25	7
63.3300018310547	6
63.4199981689453	5
63.5	3
63.5800018310547	3
63.6699981689453	4
63.75	4
63.8300018310547	1
63.9199981689453	1
64	3
64.0800018310547	2
64.1699981689453	1
64.25	4
64.3300018310547	1
64.4199981689453	3
64.5	4
64.5800018310547	2
64.6699981689453	3
64.75	3
64.8300018310547	2
64.9199981689453	3
65	3
65.0800018310547	1
65.1699981689453	2
65.25	2
65.3300018310547	2
65.4199981689453	3
65.5	3
65.5800018310547	1
65.6699981689453	3
65.75	2
65.8300018310547	2
65.9199981689453	2
66	4
66.0800018310547	4
66.1699981689453	4
66.25	7
66.3300018310547	1
66.4199981689453	5
66.5	5
66.5800018310547	6
66.6699981689453	4
66.75	2
66.8300018310547	2
66.9199981689453	1
67	8
67.0800018310547	3
67.1699981689453	2
67.25	1
67.3300018310547	2
67.4199981689453	3
67.5	3
67.5800018310547	4
67.6699981689453	4
67.75	2
67.8300018310547	2
67.9199981689453	3
68.0800018310547	2
68.1699981689453	2
68.25	2
68.3300018310547	2
68.4199981689453	2
68.5	5
68.5800018310547	4
68.6699981689453	2
68.75	5
68.9199981689453	2
69	1
69.0800018310547	3
69.1699981689453	4
69.25	2
69.3300018310547	2
69.4199981689453	2
69.5	4
69.5800018310547	3
69.6699981689453	3
69.75	2
69.8300018310547	2
69.9199981689453	1
70	2
70.0800018310547	2
70.1699981689453	2
70.3300018310547	5
70.4199981689453	2
70.5	2
70.5800018310547	2
70.6699981689453	3
70.75	4
70.8300018310547	2
70.9199981689453	3
71	1
71.0800018310547	1
71.1699981689453	3
71.25	2
71.3300018310547	4
71.4199981689453	1
71.5	1
71.5800018310547	2
71.6699981689453	5
71.75	2
71.8300018310547	2
71.9199981689453	2
72	1
72.0800018310547	3
72.1699981689453	5
72.25	2
72.3300018310547	3
72.4199981689453	3
72.5	2
72.5800018310547	1
72.6699981689453	2
72.75	4
72.8300018310547	6
73	1
73.0800018310547	3
73.1699981689453	2
73.25	1
73.3300018310547	4
73.5	3
73.5800018310547	3
73.6699981689453	4
73.75	3
73.8300018310547	4
73.9199981689453	3
74	2
74.0800018310547	2
74.1699981689453	1
74.25	2
74.3300018310547	3
74.4199981689453	1
74.5	1
74.5800018310547	2
74.6699981689453	1
74.75	2
74.9199981689453	4
75	2
75.0800018310547	7
75.1699981689453	3
75.25	4
75.3300018310547	4
75.4199981689453	2
75.5	4
75.5800018310547	4
75.6699981689453	2
75.75	1
75.8300018310547	3
76	1
76.0800018310547	3
76.1699981689453	2
76.25	2
76.3300018310547	3
76.5800018310547	1
76.6699981689453	2
76.75	4
76.8300018310547	1
76.9199981689453	2
77.0800018310547	1
77.1699981689453	1
77.25	2
77.3300018310547	1
77.4199981689453	3
77.5	3
77.5800018310547	2
77.6699981689453	1
77.75	1
77.8300018310547	3
77.9199981689453	3
78	2
78.1699981689453	2
78.25	2
78.3300018310547	2
78.4199981689453	2
78.5	1
78.5800018310547	2
78.6699981689453	1
78.75	1
78.8300018310547	3
78.9199981689453	1
79	2
79.1699981689453	1
79.25	3
79.3300018310547	1
79.4199981689453	2
79.5	2
79.5800018310547	2
79.6699981689453	1
79.75	1
79.8300018310547	2
80.0800018310547	1
80.25	1
80.3300018310547	1
80.4199981689453	1
80.5	3
80.6699981689453	1
80.75	4
80.8300018310547	2
80.9199981689453	2
81	1
81.0800018310547	2
81.1699981689453	3
81.25	2
81.3300018310547	2
81.4199981689453	2
81.5	2
81.5800018310547	3
81.6699981689453	1
81.75	1
81.9199981689453	1
82	2
82.0800018310547	2
82.1699981689453	3
82.25	2
82.3300018310547	3
82.4199981689453	3
82.5	1
82.5800018310547	1
82.6699981689453	5
82.75	1
82.8300018310547	1
82.9199981689453	1
83	3
83.0800018310547	1
83.1699981689453	3
83.25	2
83.3300018310547	3
83.4199981689453	2
83.5	1
83.5800018310547	1
83.6699981689453	4
83.9199981689453	1
84	1
84.0800018310547	1
84.1699981689453	2
84.25	1
84.3300018310547	1
84.4199981689453	1
84.6699981689453	1
84.75	1
84.8300018310547	1
85	1
85.0800018310547	1
85.25	5
85.5	4
85.5800018310547	2
85.6699981689453	1
85.8300018310547	1
85.9199981689453	1
86	3
86.0800018310547	1
86.25	1
86.3300018310547	1
86.4199981689453	1
86.5	3
86.5800018310547	1
86.6699981689453	3
86.75	1
86.9199981689453	2
87	2
87.0800018310547	1
87.1699981689453	2
87.3300018310547	2
87.5	3
87.5800018310547	1
87.9199981689453	1
88	2
88.0800018310547	1
88.25	1
88.4199981689453	1
88.5	1
88.75	1
89.3300018310547	1
89.6699981689453	1
90	1
90.25	1
90.3300018310547	1
90.6699981689453	1
90.75	2
90.8300018310547	1
91.5	1
91.5800018310547	1
91.6699981689453	1
92.0800018310547	1
92.5800018310547	1
92.9199981689453	1
93.1699981689453	1
94.9199981689453	1
96.75	1
	18592

sum(is.na(ess3$age))

## [1] 18592

ess3 <- ess3 %>% mutate(age30 = fct_drop(to_factor(ageyo30)))
ess3 %>% count(age30)

age30	n
29 or under	404
30 or older	1938
	18637

# Gender
ess3 %>% count(gndr)

gndr	n
1	9402
2	11565
	12

sum(is.na(ess3$gndr))

## [1] 12

ess3 <- ess3 %>% mutate(gndr2 = fct_drop(to_factor(gndr)))
ess3 %>% count(gndr2)

gndr2	n
Male	9402
Female	11565
	12

# Ethnicity
ess3 %>% select(contains("pray"))

pray
7
7
6
4
6
7
6
1
1
5
6
1
2
6
6
3
6
7
1
1
4
1
1
7
4
1
1
4
1
7
2
4
7
1
7
7
3
7
7
6
7
1
7
1
7
3
7
6
7
1
2
1
6
7
4
6
7
1
7
7
7
7
6
7
7
7
4
6
7
1
6
7
1
7
6
7
5
6
6
7
7
1
1
1
7
4
1
6
7
1
1
6
3
7
1
4
7
7
4
7
1
7
1
7
7
7
7
1
7
7
7
7
4
1
4
3
7
7
1
7
2
1
1
4
7
3
1
1
1
7
1
6
1
7
7
6
7
3
7
6
6
2
7
7
1
7
1
6
3
3
7
7
7
6
1
1
6
7
1
1
6
7
4
1
7
6
7
1
7
1
3
7
7
7
7
7
7
6
7
1
1
7
7
7
7
7
2
1
7
7
7
7
4
7
1
3
1
1
7
6
7
1
5
7
7
3
1
7
7
4
7
7
7
7
7
6
7
7
3
7
1
3
5
5
7
4
7
1
3
1
1
4
6
7
6
7
3
3
2
7
7
7
7
1
7
7
7
7
4
3
3
1
6
7
7
7
1
6
1
1
2
6
1
1
2
4
7
7
2
1
7
7
7
7
2
4
7
5
7
1
7
1
6
1
7
1
4
7
6
7
1
6
6
6
1
7
7
1
1
1
4
6
4
7
6
1
7
5
1
7
1
7
7
6
7
7
7
6
7
4
7
1
7
7
6
1
1
2
4
1
7
7
7
1
7
7
2
7
3
6
7
3
1
7
2
6
1
7
7
7
1
7
1
7
6
1
2
6
7
4
7
4
3
6
7
7
6
6
6
6
6
7
7
1
7
6
7
7
6
1
7
7
7
4
7
6
7
7
7
7
7
7
3
1
2
7
6
1
4
6
7
7
1
7
4
7
7
6
7
6
1
7
7
6
1
3
4
1
1
3
6
1
7
1
7
6
1
6
7
7
7
7
4
7
6
1
7
2
7
7
2
3
6
6
6
2
7
7
6
1
1
2
4
2
7
7
7
1
7
6
7
1
6
7
2
6
4
7
3
1
7
6
7
6
1
4
4
2
6
7
6
6
4
7
7
6
6
6
1
1
2
1
7
7
7
2
7
7
7
3
6
1
7
1
2
1
7
7
6
6
6
4
3
1
7
3
1
6
2
6
6
7
1
7
6
4
4
6
7
7
7
7
7
7
6
7
3
7
6
7
7
6
7
7
7
7
6
7
1
7
2
7
7
7
7
7
7
7
2
7
1
7
7
6
7
6
7
3
7
7
3
7
7
7
7
7
7
1
7
1
7
2
7
7
6
7
1
6
1
7
7
7
1
1
7
7
4
6
1
7
4
7
6
7
7
7
1
1
7
1
7
6
1
2
7
7
7
7
7
7
1
1
6
1
2
7
7
6
7
6
2
1
1
2
7
2
7
7
6
4
7
7
7
7
7
1
4
1
7
2
4
7
1
2
1
1
6
7
1
1
6
7
7
6
7
1
7
1
1
5
7
2
7
7
6
7
2
2
7
2
7
4
4
1
6
7
4
2
6
1
6
5
7
3
7
6
7
1
4
4
7
7
7
1
7
1
6
7
7
6
1
7
7
7
2
7
1
6
7
1
6
2
2
7
1
7
3
3
7
4
7
7
2
6
7
6
6
7
7
7
7
1
7
1
1
4
7
7
7
7
7
1
1
6
7
6
3
7
1
1
1
2
1
1
1
5
5
2
7
7
4
7
2
7
7
2
1
7
7
1
6
3
1
3
7
1
7
7
5
3
3
7
4
6
4
7
7
5
7
5
7
7
1
7
7
7
7
1
7
7
7
7
7
1
1
3
6
7
1
7
5
3
1
6
1
2
7
4
7
7
7
5
1
1
1
1
6
7
7
4
6
3
6
6
7
4
2
7
2
1
7
6
6
3
7
2
7
6
7
7
1
7
2
6
7
1
2
7
7
4
1
6
7
3
7
7
7
3
7
3
2
6
7
3
4
1
6
1
7
7
1
2
1
7
1
1
1
7
4
7
7
7
7
4
6
7
7
4
2
1
7
7
2
7
7
6
7
6
2
7
3
6
7
7
4
1
1
1
4
7
6
1
7
7
7
6
7
3
6
7
1
6
1
7
7
7
4
7
7
7
7
7
7
3
7
7
6
7
7
7
7
7
7
6
7
2
6
1
7
2
7
1
1
4
6
6
7
1
7
6
6
6
1
7
7
1
6
6
3
7
6
7
6
1
7
7
7
7
7
3
7
7
4
7
6
7
2
7
7
7
4
7
2
1
2
7
1
1
7
2
1
7
2
7
2
2
2
1
6
7
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ess3 <- ess3 %>% mutate(pray2 = fct_drop(to_factor(pray)))
ess3 %>% count(pray2)

pray2	n
Every day	3924
More than once a week	1349
Once a week	1113
At least once a month	1151
Only on special holy days	378
Less often	3184
Never	9771
	109

# Multicodes
svy_df <- ess3 %>% filter(!is.na(anweight)) %>% as_survey_design(weights = anweight)

The main function

Applying the function itself.

nest_multicodes <- function(
    df, # data frame 
    vars, # vars
    ci = FALSE, # Confidence intervals, set to TRUE
    caption = NULL, # Replace with caption string
    breaks = NULL, # character vector of crossbreaks
    sig = F, 
    nest = NULL,
    bases = NULL, # The object with base descriptions (from base_information() function)
    source = NULL, # A string describing the data source
    footnotes = NULL # Other character strings as footnotes, each on its own line.
) {
  
  # For nested table, define additional bits to add to table
  if(ci == F & sig == F){
  
  appendages <- function(x){x}
  
  } else if (ci == F & sig == T){
    
      appendages <- function(x){x %>% add_p(include = !all_of("unweighted_base"))}
      
  } else if (ci == T & sig == T){
    
        appendages <- function(x){x %>% 
            add_p(include = !all_of("unweighted_base")) %>% 
            add_ci(
             include = -unweighted_base,
             statistic = list(all_categorical() ~ "[{conf.low} - {conf.high}]"),
             style_fun = all_categorical() ~ label_style_sigfig(scale = 100, digits = 1)
             )
          }
        
        } else if (ci == T & sig == F){
    
        appendages <- function(x){x %>% 
            add_ci(
             include = -unweighted_base,
             statistic = list(all_categorical() ~ "[{conf.low} - {conf.high}]"),
             style_fun = all_categorical() ~ label_style_sigfig(scale = 100, digits = 1)
             )
          }
  }
  
  # Check whether survey data or not (T = survey; F = data frame)
  whether_survey_data <- if(
     class(df) %>% 
      grep("survey.design", ., value = T) %>% 
      length() > 0
  ){T} else {F}
  
  # Create object that is always the data frame, not a survey design object
  dataset_reference <- if(whether_survey_data == F){df} else {df[["variables"]]}
  
  # Check variable labels contain ": " and that the base of each variable is equal
  chk1 <- splitter_present(dataset_reference, vars)
  chk2 <- bases_equal(dataset_reference, vars)
  
  # If both checks are TRUE create table
  if (chk1 == TRUE & chk2 == TRUE) {

  # Add variable reflecting number of non-missing cases
  dataset_reference <- dataset_reference %>% 
    mutate(unweighted_base = case_when(
      !is.na(df %>% pull(vars[1])) ~ "Unweighted bases", 
      TRUE ~ NA)
      )
  var_label(dataset_reference$unweighted_base) <- ""

  # Add unweighted base variable to vector of outcomes
  var_set <- c(vars, "unweighted_base")
  
  # Extract variable labels
  variable_label_stem <- dataset_reference %>% pull(vars[1]) %>% var_label() %>% str_split_i(., ": ", 1)

  # Remove stem from variable labels
  dataset_reference <- revise_labels(dataset_reference, vars = vars)
  
  # Put data back into original object
  if(whether_survey_data == T){
    df2 <- df
    df2[["variables"]] <- dataset_reference
  } else {
    df2 <- dataset_reference
  }
  
  # Choose gtsummary function to use
  if(whether_survey_data == T){summary_fun <- tbl_svysummary} else {summary_fun <- tbl_summary}
  
  # Define base size statistic to use
  if(whether_survey_data == T){n_statistic <- "{n_unweighted}"} else {n_statistic <- "{n}"}
  
  # Create table
  tbl <- df2 %>%
    summary_fun(
      include = all_of(var_set),
      type = list(unweighted_base ~ "categorical"),
      missing = "no", 
      statistic = list(all_dichotomous() ~ "{p}", 
                        all_categorical() ~ "{p}",
                        unweighted_base ~ n_statistic), 
      digits = list(all_dichotomous() ~ 1)
    ) %>% 
    modify_header(all_stat_cols() ~ "**Total**")
  
  # Confidence intervals. 
  if (ci == TRUE) {
    tbl <- tbl %>% 
      add_ci(
       include = -unweighted_base,
       statistic = list(all_categorical() ~ "[{conf.low} - {conf.high}]"),
       style_fun = all_categorical() ~ label_style_sigfig(scale = 100, digits = 1)
       )
  }
  
    suppressWarnings({suppressMessages({
    
     brk_tbl <- 
        df2 %>%
          filter(!is.na(.data[[breaks]])) %>%
          summary_fun(
            include = all_of(var_set),
            by = all_of(breaks),
            type = list(unweighted_base ~ "categorical"),
            missing = "no", 
            statistic = list(all_dichotomous() ~ "{p}", 
                              all_categorical() ~ "{p}",
                              unweighted_base ~ n_statistic), 
            digits = list(all_dichotomous() ~ 1)
          )
  
    if(sig == TRUE){
      
      brk_tbl <- brk_tbl %>% 
          add_p(include = !all_of("unweighted_base"))
        
    } 
     
    # Confidence intervals. 
    if (ci == TRUE) {
      brk_tbl <- brk_tbl %>% 
          add_ci(
           include = -unweighted_base,
           statistic = list(all_categorical() ~ "[{conf.low} - {conf.high}]"),
           style_fun = all_categorical() ~ label_style_sigfig(scale = 100, digits = 1)
           )
    }
     
    brk_tbl <- brk_tbl %>% modify_spanning_header(
            all_stat_cols() ~ paste0("**",var_label(dataset_reference[breaks])," (overall)**"),
            # Added to ensure CI + p.value included in spanning headers
            starts_with("ci_stat") | contains("p.value") ~ paste0("**",var_label(dataset_reference[breaks])," (overall)**"), 
            ) 
     
     nest_tbl <- 
        df2 %>%
          filter(!is.na(.data[[breaks]]) & !is.na(.data[[nest]])) %>%
           tbl_strata(strata = {{nest}}, 
               ~.x %>% summary_fun(
                  include = all_of(var_set),
                  by = all_of(breaks),
                  type = list(unweighted_base ~ "categorical"),
                  missing = "no", 
                  statistic = list(all_dichotomous() ~ "{p}", 
                                    all_categorical() ~ "{p}",
                                    unweighted_base ~ n_statistic), 
                  digits = list(all_dichotomous() ~ 1)
                ) %>% 
                appendages(), 
             .header = "**{strata}**"
           )

  })})
    
  # Combine table components
  tbl <- list(tbl, brk_tbl, nest_tbl) %>% tbl_merge(tab_spanner = FALSE)
  
  # Edit caption when caption is missing
  if(is.null(caption)){
   caption <- paste0("<div style='text-align: left; font-weight: bold; color: black'>",
                 "Crosstabulation by ", 
                 var_label(dataset_reference[[breaks]]),
                 ", nested by ", 
                 var_label(dataset_reference[[nest]]),
                 ".",
                 "</div>")
  } else {
    caption <- paste0("<div style='text-align: left; font-weight: bold; color: black'>", caption,"</div>",
                "<div style='text-align: left; font-style: italic; color: black'>","<br>Crosstabulation by ", 
                 var_label(dataset_reference[[breaks]]),
                 ", nested by ", 
                 var_label(dataset_reference[[nest]]),
                 ".</div>")
  }
  
  tbl <- tbl %>%
    
    # Set headers
    modify_header(all_stat_cols() ~ "**{level}**", 
                  label ~ paste0("**",variable_label_stem,"**"), 
                  # stat_0 ~ "**%**"
                  ) %>% 
    
    # Add a caption
    modify_caption(caption)
  
    # Footnote - whether weighted or unweighted
    if(whether_survey_data == T){
           stat_types <- "All statistics shown in this table are weighted."
         } else {stat_types <- "All statistics shown in this table are unweighted."
      }

    tbl <- tbl %>%
      modify_footnote(all_stat_cols() ~ NA) %>% # Suppress standard footnotes
      modify_footnote_header(columns = "label", stat_types, replace = F)

  # Base notes, foot notes, source notes
  bases_variables <- c("multicode_labels", breaks, nest)
  
  if(!is.null(bases)) {
      
      description_of_multicode_variables <- bases[[2]] %>% unique
      names(description_of_multicode_variables) <- "multicode_labels"
      label_of_multicode_variables <- variable_label_stem
      names(label_of_multicode_variables) <- "multicode_labels"

      bases[[2]] <- c(bases[[2]], description_of_multicode_variables)
      bases[[3]] <- c(bases[[3]], label_of_multicode_variables)
      bases[[4]] <- c(bases[[4]], label_of_multicode_variables)
      
  } 
  
  footnotes <- gtsummary_table_notes(
    bases_info = bases, 
    vars = bases_variables, 
    source_note = source, 
    other_footnotes = footnotes
  )
  
  # Add any footnotes to table
  if (footnotes != ""){
    tbl <- tbl %>% modify_source_note(
    source_note = footnotes, 
    text_interpret = "html")
  }
  
  return(tbl)

  } else {
    
    helpful_error_message <- paste0("Not working: chk1 = ", chk1,", chk2 = ", chk2)
    return(helpful_error_message)
    
  }

}

writeLines(
  c("nest_multicodes <- ", # Assignment needs to be added manually
  deparse(nest_multicodes)), # Extract function code
  paste0(ddir,"/nest_multicodes.R")) # File name is just the function name 

Examples

Example 1

ess3["pray2"] %>% var_label

## $pray2
## [1] "How often pray apart from at religious services"

nest_multicodes(df = ess3, vars = int_loc, breaks = "pray2", nest = "gndr2", source = "European Social Survey")

Crosstabulation by How often pray apart from at religious services, nested by Gender.

Location able to access the internet1	Overall	How often pray apart from at religious services (overall)							Male							Female
		Every day	More than once a week	Once a week	At least once a month	Only on special holy days	Less often	Never	Every day	More than once a week	Once a week	At least once a month	Only on special holy days	Less often	Never	Every day	More than once a week	Once a week	At least once a month	Only on special holy days	Less often	Never
Home	91.0	85.2	96.6	86.0	91.4	100.0	93.2	91.8	86.2	100.0	92.9	94.7	100.0	97.1	92.1	84.8	94.9	83.3	89.7	100.0	90.2	91.4
Workplace	43.0	27.9	42.4	46.0	39.7	41.7	48.8	46.1	25.9	60.0	50.0	52.6	40.0	60.0	46.9	28.8	33.3	44.4	33.3	42.9	40.2	45.4
On the move	61.3	42.1	66.1	48.0	63.8	33.3	69.8	65.4	44.8	85.0	64.3	73.7	40.0	77.1	65.4	40.8	56.4	41.7	59.0	28.6	64.1	65.5
Some other place	51.0	38.8	44.1	50.0	56.9	25.0	53.1	54.7	37.9	55.0	64.3	73.7	20.0	54.3	52.5	39.2	38.5	44.4	48.7	28.6	52.2	56.9
None of the above	7.3	12.6	3.4	10.0	6.9	0.0	5.6	6.6	12.1	0.0	7.1	5.3	0.0	1.4	6.3	12.8	5.1	11.1	7.7	0.0	8.7	6.9
Refusal	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Don't know	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
No answer	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Unweighted bases	1,149	183	59	50	58	12	162	622	58	20	14	19	5	70	318	125	39	36	39	7	92	304
Source: European Social Survey
1 All statistics shown in this table are unweighted.

Applying base size arguments

int_loc

## [1] "acchome" "accwrk"  "accmove" "accoth"  "accnone" "accref"  "accdk"   "accna"

var_descriptions <- rep("Asked of all respondents as well, but with a separate label for testing", 8)
names(var_descriptions) <- int_loc
var_descriptions

##                                                                   acchome 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accwrk 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accmove 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accoth 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accnone 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accref 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                     accdk 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                     accna 
## "Asked of all respondents as well, but with a separate label for testing"

example_of_base_information <- base_information(
  data = ess3,
  general_base = "Asked of all respondents", 
  specific_bases = var_descriptions
)

map(example_of_base_information, head)

## [[1]]
## [1] "Asked of all respondents"
## 
## [[2]]
##                                                                   acchome 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accwrk 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accmove 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accoth 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accnone 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accref 
## "Asked of all respondents as well, but with a separate label for testing" 
## 
## [[3]]
##                                                   acchome                                                    accwrk 
##              "Location able to access the internet: Home"         "Location able to access the internet: Workplace" 
##                                                   accmove                                                    accoth 
##       "Location able to access the internet: On the move"  "Location able to access the internet: Some other place" 
##                                                   accnone                                                    accref 
## "Location able to access the internet: None of the above"           "Location able to access the internet: Refusal" 
## 
## [[4]]
##                                 name                             essround                              edition 
##                   "Title of dataset"                          "ESS round"                            "Edition" 
##                             proddate                                 idno                                cntry 
##                    "Production date" "Respondent's identification number"                            "Country"

nest_multicodes(df = ess3, vars = int_loc, breaks = c("gndr2"), nest = "pray2", ci = F, bases = example_of_base_information)

Crosstabulation by Gender, nested by How often pray apart from at religious services.

Location able to access the internet1	Overall	Gender (overall)		Every day		More than once a week		Once a week		At least once a month		Only on special holy days		Less often		Never
		Male	Female	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
Home	91.0	92.7	89.7	86.2	84.8	100.0	94.9	92.9	83.3	94.7	89.7	100.0	100.0	97.1	90.2	92.1	91.4
Workplace	43.0	47.0	39.8	25.9	28.8	60.0	33.3	50.0	44.4	52.6	33.3	40.0	42.9	60.0	40.2	46.9	45.4
On the move	61.3	65.6	57.9	44.8	40.8	85.0	56.4	64.3	41.7	73.7	59.0	40.0	28.6	77.1	64.1	65.4	65.5
Some other place	51.0	52.0	50.2	37.9	39.2	55.0	38.5	64.3	44.4	73.7	48.7	20.0	28.6	54.3	52.2	52.5	56.9
None of the above	7.3	5.9	8.4	12.1	12.8	0.0	5.1	7.1	11.1	5.3	7.7	0.0	0.0	1.4	8.7	6.3	6.9
Refusal	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Don't know	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
No answer	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Unweighted bases	1,149	506	643	58	125	20	39	14	36	19	39	5	7	70	92	318	304
Variable base descriptions: Asked of all respondents as well, but with a separate label for testing: Location able to access the internet All other variables: Asked of all respondents
1 All statistics shown in this table are unweighted.